Postman 컬렉션 및 요청으로부터 AI 에이전트 도구를 생성하는 MCP 서버입니다. 이 서버는 Postman API와 통합되어 API 엔드포인트를 다양한 AI 프레임워크에서 사용할 수 있는 유형 안전 코드로 변환합니다.
모델 컨텍스트 프로토콜(MCP)은 대규모 언어 모델(LLM)과 외부 시스템 간의 컨텍스트를 관리하기 위한 새로운 표준화된 프로토콜 입니다. 이 저장소에서는 Postman 도구 생성 API를 위한 설치 프로그램과 MCP 서버를 제공합니다.
이를 통해 Claude Desktop 이나 Cline 과 같은 MCP 클라이언트를 사용하여 자연어를 사용하여 Postman 계정에서 다음과 같은 작업을 수행할 수 있습니다.
Create an AI tool for: collectionID: 12345-abcde requestID: 67890-fghij typescript openai
Postman 컬렉션에서 TypeScript/JavaScript 코드 생성
다양한 AI 프레임워크 지원(OpenAI, Mistral, Gemini, Anthropic, LangChain, AutoGen)
유형 안전 코드 생성
오류 처리 및 응답 검증
Related MCP server: Postman MCP Generator
종속성 설치:
지엑스피1
서버를 빌드하세요:
npm run buildClaude 설정 파일( cline_mcp_settings.json )에 다음을 추가하여 MCP 설정을 구성합니다.
{
"mcpServers": {
"postman-ai-tools": {
"command": "node",
"args": [
"/path/to/postman-tool-generation-server/build/index.js"
],
"env": {
"POSTMAN_API_KEY": "your-postman-api-key"
},
"disabled": false,
"autoApprove": []
}
}
}서버는 다음 매개변수를 사용하여 generate_ai_tool 이라는 단일 도구를 제공합니다.
{
collectionId: string; // The Public API Network collection ID
requestId: string; // The public request ID
language: "javascript" | "typescript"; // Programming language to use
agentFramework: "openai" | "mistral" | "gemini" | "anthropic" | "langchain" | "autogen"; // AI framework
}// Using the tool through MCP
const result = await use_mcp_tool({
server_name: "postman-ai-tools",
tool_name: "generate_ai_tool",
arguments: {
collectionId: "your-collection-id",
requestId: "your-request-id",
language: "typescript",
agentFramework: "openai"
}
});이 도구는 다음을 포함하는 유형 안전 코드를 생성합니다.
요청/응답에 대한 유형 정의
오류 처리
API 통합
OpenAI 함수 정의
문서 및 예제
종속성 설치:
npm installsrc/index.ts 를 변경하세요
서버를 빌드하세요:
npm run build업데이트된 서버를 로드하려면 Claude 앱을 다시 시작하세요.
POSTMAN_API_KEY : Postman API 키(필수)
서버에는 다음에 대한 포괄적인 오류 처리 기능이 포함되어 있습니다.
잘못된 매개변수
API 실패
JSON 구문 분석 오류
네트워크 문제
오류 응답에는 문제 진단에 도움이 되는 자세한 메시지가 포함되어 있습니다.
기여를 환영합니다! 풀 리퀘스트를 제출해 주세요.
MIT 라이센스
Generate code for an AI agent tool using a Postman collection and request
| Name | Required | Description | Default |
|---|---|---|---|
| collectionId | Yes | The Public API Network collection ID | |
| requestId | Yes | The public request ID | |
| language | Yes | Programming language to use | |
| agentFramework | Yes | AI agent framework to use |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool 'generate[s] code' but does not clarify aspects like whether this is a read-only operation, if it requires authentication, potential side effects, or output format. This leaves significant gaps in understanding the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence that efficiently conveys the tool's purpose without unnecessary words. It is front-loaded and every part of the sentence contributes directly to understanding, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a code generation tool with no annotations and no output schema, the description is insufficient. It lacks details on what the generated code includes, how it handles errors, or the format of the output, leaving the agent with incomplete information to use the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so the input schema already documents all parameters thoroughly. The description adds no additional meaning beyond what the schema provides, such as examples or usage context for the parameters. This meets the baseline for high schema coverage but does not enhance parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('generate code') and resources ('AI agent tool'), specifying the input sources ('Postman collection and request'). It distinguishes what the tool does without ambiguity, making it immediately understandable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by mentioning the input sources (Postman collection and request), but it does not provide explicit guidance on when to use this tool versus alternatives, prerequisites, or exclusions. Since there are no sibling tools, the lack of comparative guidance is less critical, but it still lacks detailed context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.0generate_ai_toolScored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap with other tools. The tool's purpose is clearly defined and distinct by default.
A single tool inherently has perfect naming consistency, as there are no other tools to compare it against for patterns or conventions.
One tool is too few for the server's stated purpose of 'Postman Tool Generation,' which implies a broader scope like generating, managing, or testing tools. A single generation tool feels thin and incomplete for this domain.
The tool surface is severely incomplete for the inferred domain of Postman-based tool generation. There are obvious gaps, such as no tools for listing, editing, deleting, or testing generated tools, which limits agent workflows to a single action.
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